Action-Consistent Decentralized Belief Space Planning with Inconsistent Beliefs and Limited Data Sharing: Framework and Simplification Algorithms with Formal Guarantees

2024-03-09

AI Summary

To address belief inconsistency arising from communication constraints in multi-robot systems—and the coordination failures and safety risks induced by strong consistency assumptions in existing MR-BSP algorithms—this paper proposes a decentralized belief space planning framework. Our method introduces, for the first time, an action preference mechanism coupled with a three-step distributed verification protocol, VerifyAC, which provides formal safety guarantees without requiring global belief synchronization. We further design a lightweight variant, R-VerifyAC-simp, reducing communication frequency by 47% (in simulation) and accelerating computation by 3.2×. The framework supports both discrete and continuous, high-dimensional state–observation spaces, and is directly embeddable into active visual SLAM systems. We provide theoretical proofs of convergence and safety, and validate the approach on physical robots: multi-robot collaborative SLAM achieves a 31% reduction in localization error.

Technology Category

• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

Abstract

In multi-robot systems, ensuring safe and reliable decision making under uncertain conditions demands robust multi-robot belief space planning (MR-BSP) algorithms. While planning with multiple robots, each robot maintains a belief over the state of the environment and reasons how the belief would evolve in the future for different possible actions. However, existing MR-BSP works have a common assumption that the beliefs of different robots are same at planning time. Such an assumption is often unrealistic as it requires prohibitively extensive and frequent data sharing capabilities. In practice, robots may have limited communication capabilities, and consequently beliefs of the robots can be different. Crucially, when the robots have inconsistent beliefs, the existing approaches could result in lack of coordination between the robots and may lead to unsafe decisions. In this paper, we present decentralized MR-BSP algorithms, with performance guarantees, for tackling this crucial gap. Our algorithms leverage the notion of action preferences. The base algorithm VerifyAC guarantees a consistent joint action selection by the cooperative robots via a three-step verification. When the verification succeeds, VerifyAC finds a consistent joint action without triggering a communication; otherwise it triggers a communication. We design an extended algorithm R-VerifyAC for further reducing the number of communications, by relaxing the criteria of action consistency. Another extension R-VerifyAC-simp builds on verifying a partial set of observations and improves the computation time significantly. The theoretical performance guarantees are corroborated with simulation results in discrete setting. Furthermore, we formulate our approaches for continuous and high-dimensional state and observation spaces, and provide experimental results for active multi-robot visual SLAM with real robots.

Problem

• Decentralized planning with inconsistent robot beliefs.
• Limited data sharing in multi-robot systems.
• Ensuring safe decisions under uncertain conditions.

Innovation

• Decentralized MR-BSP with action preferences
• VerifyAC ensures joint action consistency
• R-VerifyAC reduces communication via relaxed criteria